DOCSLIB.ORG

Contents 3 Inner Product Spaces

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Contents 3 Inner Product Spaces Linear Algebra (part 3) : Inner Product Spaces (by Evan Dummit, 2020, v. 2.00) Contents 3 Inner Product Spaces 1 3.1 Inner Product Spaces . 1 3.1.1 Inner Products on Real Vector Spaces . 1 3.1.2 Inner Products on Complex Vector Spaces . 3 3.1.3 Properties of Inner Products, Norms . 4 3.2 Orthogonality . 6 3.2.1 Orthogonality, Orthonormal Bases, and the Gram-Schmidt Procedure . 7 3.2.2 Orthogonal Complements and Orthogonal Projection . 9 3.3 Applications of Inner Products . 13 3.3.1 Least-Squares Estimates . 13 3.3.2 Fourier Series . 16 3.4 Linear Transformations and Inner Products . 18 3.4.1 Characterizations of Inner Products . 18 3.4.2 The Adjoint of a Linear Transformation . 20 3 Inner Product Spaces In this chapter we will study vector spaces having an additional kind of structure called an inner product, which generalizes the idea of the dot product of vectors in Rn, and which will allow us to formulate notions of length and angle in more general vector spaces. We dene inner products in real and complex vector spaces and establish some of their properties, including the celebrated Cauchy-Schwarz inequality, and survey some applications. We then discuss orthogonality of vectors and subspaces and in particular describe a method for constructing an orthonormal basis for any nite-dimensional inner product space, which provides an analogue of giving standard unit coordinate axes in Rn. Next, we discuss a pair of very important practical applications of inner products and orthogonality: computing least-squares approximations and approximating periodic functions with Fourier series. We close with a discussion of the interplay between linear transformations and inner products. 3.1 Inner Product Spaces • Notation: In this chapter, we will use parentheses around vectors rather than angle brackets, since we will shortly be using angle brackets to denote an inner product. We will also avoid using dots when discussing scalar multiplication, and reserve the dot notation for the dot product of two vectors. 3.1.1 Inner Products on Real Vector Spaces • Our rst goal is to describe the natural analogue in an arbitrary real vector space of the dot product in Rn. We recall some basic properties of the dot product: ◦ The dot product distributes over addition and scaling: (v1 +cv2)·w = (v1 ·w)+c(v2 ·w) for any vectors v1; v2; w and scalar c. 1 ◦ The dot product is commutative: v · w = w · v for any vectors v; w. ◦ The dot product is nonnegative: v · v ≥ 0 for any vector v. • We will use these three properties to dene an arbitrary inner product on a real vector space: • Denition: If V is a real vector space, an inner product on V is a pairing that assigns a scalar in F to each ordered pair (v; w) of vectors in V . This pairing is denoted hv; wi and must satisfy the following properties: [I1] Linearity in the rst argument: hv1 + cv2; wi = hv1; wi + chv2; wi. [I2] Symmetry: hw; vi = hv; wi. [I3] Positive-deniteness: hv; vi ≥ 0 for all v, and hv; vi = 0 only when v = 0. ◦ The linearity and symmetry properties are fairly clear: if we x the second component, the inner product behaves like a linear function in the rst component, and we want both components to behave in the same way. ◦ The positive-deniteness property is intended to capture an idea about length: namely, the length of a vector v should be the (inner) product of v with itself, and lengths are supposed to be nonnegative. Furthermore, the only vector of length zero should be the zero vector. • Denition: A vector space V together with an inner product h·; ·i on V is called an inner product space. ◦ Any given vector space may have many dierent inner products. ◦ When we say Let V be an inner product space, we intend this to mean that V is equipped with a particular (xed) inner product. • The entire purpose of dening an inner product is to generalize the notion of the dot product to more general vector spaces, so we should check that the dot product on Rn is actually an inner product: Example: Show that the standard dot product on n, dened as • R (x1; : : : ; xn)·(y1; : : : ; yn) = x1y1 +···+xnyn is an inner product. ◦ [I1]-[I2]: We have previously veried the linearity and symmetry properties. [I3]: If then 2 2 2 . Since each square is nonnegative, , and ◦ v = (x1; : : : ; xn) v · v = x1 + x2 + ··· + xn v · v ≥ 0 v · v = 0 only when all of the components of v are zero. • There are other examples of inner products on Rn beyond the standard dot product. Example: Show that the pairing on 2 is an inner product. • h(x1; y1); (x2; y2)i = 3x1x2 +2x1y2 +2x2y1 +4y1y2 R ◦ [I1]-[I2]: It is an easy algebraic computation to verify the linearity and symmetry properties. ◦ [I3]: We have h(x; y); (x; y)i = 3x2 + 4xy + 4y2 = 2x2 + (x + 2y)2, and since each square is nonnegative, the inner product is always nonnegative. Furthermore, it equals zero only when both squares are zero, and this clearly only occurs for x = y = 0. • We can dene another class of inner products on function spaces using integration: • Example: Let V be the vector space of continuous (real-valued) functions on the interval [a; b]. Show that b is an inner product on . hf; gi = a f(x)g(x) dx V ´ [I1]: We have b b b ◦ hf1 + cf2; gi = a [f1(x) + cf2(x)] g(x) dx = a f1(x)g(x) dx + c a f2(x)g(x) dx = hf1; gi + c hf2; gi. ´ ´ ´ [I2]: Observe that b b . ◦ hg; fi = a g(x)f(x) dx = a f(x)g(x) dx = hf; gi [I3]: Notice that b´ 2 is the integral´ of a nonnegative function, so it is always nonnegative. ◦ hf; fi = a f(x) dx Furthermore (since f is assumed´ to be continuous) the integral of f 2 cannot be zero unless f is identically zero. ◦ Remark: More generally, if w(x) is any xed positive (weight) function that is continuous on [a; b], b is an inner product on . hf; gi = a f(x)g(x) · w(x) dx V ´ 2 3.1.2 Inner Products on Complex Vector Spaces • We would now like to extend the notion of an inner product to complex vector spaces. ◦ A natural rst guess would be to use the same denition as in a real vector space. However, this turns out not to be the right choice! ◦ To explain why, suppose that we try to use the same denition of dot product to nd the length of a vector of complex numbers, i.e., by computing v · v. ◦ We can see that the dot product of the vector (1; 0; i) with itself is 0, but this vector certainly is not the zero vector. Even worse, the dot product of (1; 0; 2i) with itself is −3, while the dot product of (1; 0; 1 + 2i) with itself is −2 + 4i. None of these choices for lengths seem sensible. p ◦ Indeed, a more natural choice of length for the vector (1; 0; i) is 2: the rst component has absolute value 1, the second has absolute value 0, and the last has absolute value 1, for an overall length of p p 2 2 2, in analogy with the real vector which also has length . Similarly, 1 + 0 + 1 p p (1; 0; 1) 2 (1; 0; 2i) should really have length 12 + 02 + 22 = 5. ◦ One way to obtain a nonnegative function that seems to capture this idea of length for a complex vector is to include a conjugation: notice that for any complex vector v, the dot product v · v will always be a nonnegative real number. ◦ Using the example above, we can compute that (1; 0; i) · (1; 0; i) = 12 + 02 + 12 = 2, so this modied dot product seems to give the square of the length of a complex vector, at least in this one case. 1 ◦ All of this suggests that the right analogy of the Rn dot product for a pair of complex vectors v, w is v · w rather than v · w. ◦ We then lose the symmetry property of the real inner product, since now v · w and w · v are not equal in general. But notice we still have a relation between v · w and w · v: namely, the latter is the complex conjugate of the former. ◦ We can therefore obtain the right denition by changing the symmetry property hw; vi = hv; wi to conjugate-symmetry: hw; vi = hv; wi . • With this minor modication, we can extend the idea of an inner product to a complex vector space: • Denition: If V is a (real or) complex vector space, an inner product on V is a pairing that assigns a scalar in F to each ordered pair (v; w) of vectors in V . This pairing is denoted hv; wi and must satisfy the following properties: [I1] Linearity in the rst argument: hv1 + cv2; wi = hv1; wi + chv2; wi. [I2] Conjugate-symmetry: hw; vi = hv; wi (where the bar denotes the complex conjugate). [I3] Positive-deniteness: hv; vi ≥ 0 for all v, and hv; vi = 0 only when v = 0. ◦ This denition generalizes the one we gave for a real vector space earlier: if V is a real vector space then hw; vi = hw; vi since hw; vi is always a real number. ◦ Important Warning: In other disciplines (particularly physics), inner products are often dened to be linear in the second argument, rather than the rst.
Load more

Recommended publications
  • Math 317: Linear Algebra Notes and Problems
    Math 317: Linear Algebra Notes and Problems Nicholas Long SFASU Contents Introduction iv 1 Efficiently Solving Systems of Linear Equations and Matrix Op- erations 1 1.1 Warmup Problems . .1 1.2 Solving Linear Systems . .3 1.2.1 Geometric Interpretation of a Solution Set . .9 1.3 Vector and Matrix Equations . 11 1.4 Solution Sets of Linear Systems . 15 1.5 Applications . 18 1.6 Matrix operations . 20 1.6.1 Special Types of Matrices . 21 1.6.2 Matrix Multiplication . 22 2 Vector Spaces 25 2.1 Subspaces . 28 2.2 Span . 29 2.3 Linear Independence . 31 2.4 Linear Transformations . 33 2.5 Applications . 38 3 Connecting Ideas 40 3.1 Basis and Dimension . 40 3.1.1 rank and nullity . 41 3.1.2 Coordinate Vectors relative to a basis . 42 3.2 Invertible Matrices . 44 3.2.1 Elementary Matrices . 44 ii CONTENTS iii 3.2.2 Computing Inverses . 45 3.2.3 Invertible Matrix Theorem . 46 3.3 Invertible Linear Transformations . 47 3.4 LU factorization of matrices . 48 3.5 Determinants . 49 3.5.1 Computing Determinants . 49 3.5.2 Properties of Determinants . 51 3.6 Eigenvalues and Eigenvectors . 52 3.6.1 Diagonalizability . 55 3.6.2 Eigenvalues and Eigenvectors of Linear Transfor- mations . 56 4 Inner Product Spaces 58 4.1 Inner Products . 58 4.2 Orthogonal Complements . 59 4.3 QR Decompositions . 59 Nicholas Long Introduction In this course you will learn about linear algebra by solving a carefully designed sequence of problems. It is important that you understand every problem and proof.
    [Show full text]
  • Notes, Since the Quadratic Form Associated with an Inner Product (Kuk2 = Hu, Ui) Must Be Positive Def- Inite
    Bindel, Spring 2012 Intro to Scientific Computing (CS 3220) Week 3: Wednesday, Feb 8 Spaces and bases 1 n I have two favorite vector spaces : R and the space Pd of polynomials of degree at most d. For Rn, we have a canonical basis: n R = spanfe1; e2; : : : ; eng; where ek is the kth column of the identity matrix. This basis is frequently convenient both for analysis and for computation. For Pd, an obvious- seeming choice of basis is the power basis: 2 d Pd = spanf1; x; x ; : : : ; x g: But this obvious-looking choice turns out to often be terrible for computation. Why? The short version is that powers of x aren't all that strongly linearly dependent, but we need to develop some more concepts before that short description will make much sense. The range space of a matrix or a linear map A is just the set of vectors y that can be written in the form y = Ax. If A is full (column) rank, then the columns of A are linearly independent, and they form a basis for the range space. Otherwise, A is rank-deficient, and there is a non-trivial null space consisting of vectors x such that Ax = 0. Rank deficiency is a delicate property2. For example, consider the matrix 1 1 A = : 1 1 This matrix is rank deficient, but the matrix 1 + δ 1 A^ = : 1 1 is not rank deficient for any δ 6= 0. Technically, the columns of A^ form a basis for R2, but we should be disturbed by the fact that A^ is so close to a singular matrix.
    [Show full text]
  • Introduction to Linear Bialgebra
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of New Mexico University of New Mexico UNM Digital Repository Mathematics and Statistics Faculty and Staff Publications Academic Department Resources 2005 INTRODUCTION TO LINEAR BIALGEBRA Florentin Smarandache University of New Mexico, [email protected] W.B. Vasantha Kandasamy K. Ilanthenral Follow this and additional works at: https://digitalrepository.unm.edu/math_fsp Part of the Algebra Commons, Analysis Commons, Discrete Mathematics and Combinatorics Commons, and the Other Mathematics Commons Recommended Citation Smarandache, Florentin; W.B. Vasantha Kandasamy; and K. Ilanthenral. "INTRODUCTION TO LINEAR BIALGEBRA." (2005). https://digitalrepository.unm.edu/math_fsp/232 This Book is brought to you for free and open access by the Academic Department Resources at UNM Digital Repository. It has been accepted for inclusion in Mathematics and Statistics Faculty and Staff Publications by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected], [email protected], [email protected]. INTRODUCTION TO LINEAR BIALGEBRA W. B. Vasantha Kandasamy Department of Mathematics Indian Institute of Technology, Madras Chennai – 600036, India e-mail: [email protected] web: http://mat.iitm.ac.in/~wbv Florentin Smarandache Department of Mathematics University of New Mexico Gallup, NM 87301, USA e-mail: [email protected] K. Ilanthenral Editor, Maths Tiger, Quarterly Journal Flat No.11, Mayura Park, 16, Kazhikundram Main Road, Tharamani, Chennai – 600 113, India e-mail: [email protected] HEXIS Phoenix, Arizona 2005 1 This book can be ordered in a paper bound reprint from: Books on Demand ProQuest Information & Learning (University of Microfilm International) 300 N.
    [Show full text]
  • The Matroid Theorem We First Review Our Definitions: a Subset System Is A
    CMPSCI611: The Matroid Theorem Lecture 5 We first review our definitions: A subset system is a set E together with a set of subsets of E, called I, such that I is closed under inclusion. This means that if X ⊆ Y and Y ∈ I, then X ∈ I. The optimization problem for a subset system (E, I) has as input a positive weight for each element of E. Its output is a set X ∈ I such that X has at least as much total weight as any other set in I. A subset system is a matroid if it satisfies the exchange property: If i and i0 are sets in I and i has fewer elements than i0, then there exists an element e ∈ i0 \ i such that i ∪ {e} ∈ I. 1 The Generic Greedy Algorithm Given any finite subset system (E, I), we find a set in I as follows: • Set X to ∅. • Sort the elements of E by weight, heaviest first. • For each element of E in this order, add it to X iff the result is in I. • Return X. Today we prove: Theorem: For any subset system (E, I), the greedy al- gorithm solves the optimization problem for (E, I) if and only if (E, I) is a matroid. 2 Theorem: For any subset system (E, I), the greedy al- gorithm solves the optimization problem for (E, I) if and only if (E, I) is a matroid. Proof: We will show first that if (E, I) is a matroid, then the greedy algorithm is correct. Assume that (E, I) satisfies the exchange property.
    [Show full text]
  • Determinant Notes
    68 UNIT FIVE DETERMINANTS 5.1 INTRODUCTION In unit one the determinant of a 2× 2 matrix was introduced and used in the evaluation of a cross product. In this chapter we extend the definition of a determinant to any size square matrix. The determinant has a variety of applications. The value of the determinant of a square matrix A can be used to determine whether A is invertible or noninvertible. An explicit formula for A–1 exists that involves the determinant of A. Some systems of linear equations have solutions that can be expressed in terms of determinants. 5.2 DEFINITION OF THE DETERMINANT a11 a12 Recall that in chapter one the determinant of the 2× 2 matrix A = was a21 a22 defined to be the number a11a22 − a12 a21 and that the notation det (A) or A was used to represent the determinant of A. For any given n × n matrix A = a , the notation A [ ij ]n×n ij will be used to denote the (n −1)× (n −1) submatrix obtained from A by deleting the ith row and the jth column of A. The determinant of any size square matrix A = a is [ ij ]n×n defined recursively as follows. Definition of the Determinant Let A = a be an n × n matrix. [ ij ]n×n (1) If n = 1, that is A = [a11], then we define det (A) = a11 . n 1k+ (2) If na>=1, we define det(A) ∑(-1)11kk det(A ) k=1 Example If A = []5 , then by part (1) of the definition of the determinant, det (A) = 5.
    [Show full text]
  • Eigenvalues and Eigenvectors
    Jim Lambers MAT 605 Fall Semester 2015-16 Lecture 14 and 15 Notes These notes correspond to Sections 4.4 and 4.5 in the text. Eigenvalues and Eigenvectors In order to compute the matrix exponential eAt for a given matrix A, it is helpful to know the eigenvalues and eigenvectors of A. Definitions and Properties Let A be an n × n matrix. A nonzero vector x is called an eigenvector of A if there exists a scalar λ such that Ax = λx: The scalar λ is called an eigenvalue of A, and we say that x is an eigenvector of A corresponding to λ. We see that an eigenvector of A is a vector for which matrix-vector multiplication with A is equivalent to scalar multiplication by λ. Because x is nonzero, it follows that if x is an eigenvector of A, then the matrix A − λI is singular, where λ is the corresponding eigenvalue. Therefore, λ satisfies the equation det(A − λI) = 0: The expression det(A−λI) is a polynomial of degree n in λ, and therefore is called the characteristic polynomial of A (eigenvalues are sometimes called characteristic values). It follows from the fact that the eigenvalues of A are the roots of the characteristic polynomial that A has n eigenvalues, which can repeat, and can also be complex, even if A is real. However, if A is real, any complex eigenvalues must occur in complex-conjugate pairs. The set of eigenvalues of A is called the spectrum of A, and denoted by λ(A). This terminology explains why the magnitude of the largest eigenvalues is called the spectral radius of A.
    [Show full text]
  • Algebra of Linear Transformations and Matrices Math 130 Linear Algebra
    Then the two compositions are 0 −1 1 0 0 1 BA = = 1 0 0 −1 1 0 Algebra of linear transformations and 1 0 0 −1 0 −1 AB = = matrices 0 −1 1 0 −1 0 Math 130 Linear Algebra D Joyce, Fall 2013 The products aren't the same. You can perform these on physical objects. Take We've looked at the operations of addition and a book. First rotate it 90◦ then flip it over. Start scalar multiplication on linear transformations and again but flip first then rotate 90◦. The book ends used them to define addition and scalar multipli- up in different orientations. cation on matrices. For a given basis β on V and another basis γ on W , we have an isomorphism Matrix multiplication is associative. Al- γ ' φβ : Hom(V; W ) ! Mm×n of vector spaces which though it's not commutative, it is associative. assigns to a linear transformation T : V ! W its That's because it corresponds to composition of γ standard matrix [T ]β. functions, and that's associative. Given any three We also have matrix multiplication which corre- functions f, g, and h, we'll show (f ◦ g) ◦ h = sponds to composition of linear transformations. If f ◦ (g ◦ h) by showing the two sides have the same A is the standard matrix for a transformation S, values for all x. and B is the standard matrix for a transformation T , then we defined multiplication of matrices so ((f ◦ g) ◦ h)(x) = (f ◦ g)(h(x)) = f(g(h(x))) that the product AB is be the standard matrix for S ◦ T .
    [Show full text]
  • Scaling Algorithms and Tropical Methods in Numerical Matrix Analysis
    Scaling Algorithms and Tropical Methods in Numerical Matrix Analysis: Application to the Optimal Assignment Problem and to the Accurate Computation of Eigenvalues Meisam Sharify To cite this version: Meisam Sharify. Scaling Algorithms and Tropical Methods in Numerical Matrix Analysis: Application to the Optimal Assignment Problem and to the Accurate Computation of Eigenvalues. Numerical Analysis [math.NA]. Ecole Polytechnique X, 2011. English. pastel-00643836 HAL Id: pastel-00643836 https://pastel.archives-ouvertes.fr/pastel-00643836 Submitted on 24 Nov 2011 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Th`esepr´esent´eepour obtenir le titre de DOCTEUR DE L'ECOLE´ POLYTECHNIQUE Sp´ecialit´e: Math´ematiquesAppliqu´ees par Meisam Sharify Scaling Algorithms and Tropical Methods in Numerical Matrix Analysis: Application to the Optimal Assignment Problem and to the Accurate Computation of Eigenvalues Jury Marianne Akian Pr´esident du jury St´ephaneGaubert Directeur Laurence Grammont Examinateur Laura Grigori Examinateur Andrei Sobolevski Rapporteur Fran¸coiseTisseur Rapporteur Paul Van Dooren Examinateur September 2011 Abstract Tropical algebra, which can be considered as a relatively new field in Mathemat- ics, emerged in several branches of science such as optimization, synchronization of production and transportation, discrete event systems, optimal control, oper- ations research, etc.
    [Show full text]
  • Determinants Linear Algebra MATH 2010
    Determinants Linear Algebra MATH 2010 • Determinants can be used for a lot of different applications. We will look at a few of these later. First of all, however, let's talk about how to compute a determinant. • Notation: The determinant of a square matrix A is denoted by jAj or det(A). • Determinant of a 2x2 matrix: Let A be a 2x2 matrix a b A = c d Then the determinant of A is given by jAj = ad − bc Recall, that this is the same formula used in calculating the inverse of A: 1 d −b 1 d −b A−1 = = ad − bc −c a jAj −c a if and only if ad − bc = jAj 6= 0. We will later see that if the determinant of any square matrix A 6= 0, then A is invertible or nonsingular. • Terminology: For larger matrices, we need to use cofactor expansion to find the determinant of A. First of all, let's define a few terms: { Minor: A minor, Mij, of the element aij is the determinant of the matrix obtained by deleting the ith row and jth column. ∗ Example: Let 2 −3 4 2 3 A = 4 6 3 1 5 4 −7 −8 Then to find M11, look at element a11 = −3. Delete the entire column and row that corre- sponds to a11 = −3, see the image below. Then M11 is the determinant of the remaining matrix, i.e., 3 1 M11 = = −8(3) − (−7)(1) = −17: −7 −8 ∗ Example: Similarly, to find M22 can be found looking at the element a22 and deleting the same row and column where this element is found, i.e., deleting the second row, second column: Then −3 2 M22 = = −8(−3) − 4(−2) = 16: 4 −8 { Cofactor: The cofactor, Cij is given by i+j Cij = (−1) Mij: Basically, the cofactor is either Mij or −Mij where the sign depends on the location of the element in the matrix.
    [Show full text]
  • CONTINUITY in the ALEXIEWICZ NORM Dedicated to Prof. J
    131 (2006) MATHEMATICA BOHEMICA No. 2, 189{196 CONTINUITY IN THE ALEXIEWICZ NORM Erik Talvila, Abbotsford (Received October 19, 2005) Dedicated to Prof. J. Kurzweil on the occasion of his 80th birthday Abstract. If f is a Henstock-Kurzweil integrable function on the real line, the Alexiewicz norm of f is kfk = sup j I fj where the supremum is taken over all intervals I ⊂ . Define I the translation τx by τxfR(y) = f(y − x). Then kτxf − fk tends to 0 as x tends to 0, i.e., f is continuous in the Alexiewicz norm. For particular functions, kτxf − fk can tend to 0 arbitrarily slowly. In general, kτxf − fk > osc fjxj as x ! 0, where osc f is the oscillation of f. It is shown that if F is a primitive of f then kτxF − F k kfkjxj. An example 1 6 1 shows that the function y 7! τxF (y) − F (y) need not be in L . However, if f 2 L then kτxF − F k1 6 kfk1jxj. For a positive weight function w on the real line, necessary and sufficient conditions on w are given so that k(τxf − f)wk ! 0 as x ! 0 whenever fw is Henstock-Kurzweil integrable. Applications are made to the Poisson integral on the disc and half-plane. All of the results also hold with the distributional Denjoy integral, which arises from the completion of the space of Henstock-Kurzweil integrable functions as a subspace of Schwartz distributions. Keywords: Henstock-Kurzweil integral, Alexiewicz norm, distributional Denjoy integral, Poisson integral MSC 2000 : 26A39, 46Bxx 1.
    [Show full text]
  • Math 217: Multilinearity of Determinants Professor Karen Smith (C)2015 UM Math Dept Licensed Under a Creative Commons By-NC-SA 4.0 International License
    Math 217: Multilinearity of Determinants Professor Karen Smith (c)2015 UM Math Dept licensed under a Creative Commons By-NC-SA 4.0 International License. A. Let V −!T V be a linear transformation where V has dimension n. 1. What is meant by the determinant of T ? Why is this well-defined? Solution note: The determinant of T is the determinant of the B-matrix of T , for any basis B of V . Since all B-matrices of T are similar, and similar matrices have the same determinant, this is well-defined—it doesn't depend on which basis we pick. 2. Define the rank of T . Solution note: The rank of T is the dimension of the image. 3. Explain why T is an isomorphism if and only if det T is not zero. Solution note: T is an isomorphism if and only if [T ]B is invertible (for any choice of basis B), which happens if and only if det T 6= 0. 3 4. Now let V = R and let T be rotation around the axis L (a line through the origin) by an 21 0 0 3 3 angle θ. Find a basis for R in which the matrix of ρ is 40 cosθ −sinθ5 : Use this to 0 sinθ cosθ compute the determinant of T . Is T othogonal? Solution note: Let v be any vector spanning L and let u1; u2 be an orthonormal basis ? for V = L . Rotation fixes ~v, which means the B-matrix in the basis (v; u1; u2) has 213 first column 405.
    [Show full text]
  • Determinants Math 122 Calculus III D Joyce, Fall 2012
    Determinants Math 122 Calculus III D Joyce, Fall 2012 What they are. A determinant is a value associated to a square array of numbers, that square array being called a square matrix. For example, here are determinants of a general 2 × 2 matrix and a general 3 × 3 matrix. a b = ad − bc: c d a b c d e f = aei + bfg + cdh − ceg − afh − bdi: g h i The determinant of a matrix A is usually denoted jAj or det (A). You can think of the rows of the determinant as being vectors. For the 3×3 matrix above, the vectors are u = (a; b; c), v = (d; e; f), and w = (g; h; i). Then the determinant is a value associated to n vectors in Rn. There's a general definition for n×n determinants. It's a particular signed sum of products of n entries in the matrix where each product is of one entry in each row and column. The two ways you can choose one entry in each row and column of the 2 × 2 matrix give you the two products ad and bc. There are six ways of chosing one entry in each row and column in a 3 × 3 matrix, and generally, there are n! ways in an n × n matrix. Thus, the determinant of a 4 × 4 matrix is the signed sum of 24, which is 4!, terms. In this general definition, half the terms are taken positively and half negatively. In class, we briefly saw how the signs are determined by permutations.
    [Show full text]